Systems for generating vectors for stock keeping units using a neural network

ABSTRACT

Systems and methods are disclosed for displaying information related to items in a store. Stock keeping unit (SKU) information for items in the store and combination information indicating which SKUs were sold together can be accepted. Dimension information and structure information for a neural network can be defined. The neural network can be trained using the combination information indicating which SKUs were sold together in a same combination to calculate weight values and bias values for the neural network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claim priority to Provisional Application No. 63/003,527 filed Apr. 1, 2020. The entirety of the above-listed application is incorporated herein by reference.

BACKGROUND

All of the following background articles are hereby incorporated by reference in their entireties:

-   Conneau, A., Lample, G., Ranzato, M. A., Denoyer, L., & Jégou, H.     (2017). Word translation without parallel data. arXiv preprint     arXiv:1710.04087. -   Yin, Z., & Shen, Y. (2018). On the dimensionality of word embedding.     In Advances in Neural Information Processing Systems (pp. 887-898). -   Mikolov, Tomas, et al. “Distributed representations of words and     phrases and their compositionality.” Advances in neural information     processing systems. 2013. -   Mikolov, Tomas, et al. “Efficient estimation of word representations     in vector space.” arXiv preprint arXiv:1301.3781 (2013). -   Le, Quoc, and Tomas Mikolov. “Distributed representations of     sentences and documents.” International conference on machine     learning. 2014 -   A Gentle Introduction to Doc2Ved:     https://medium.com/wisio/a-gentle-introduction-to-doc2vec-db3e8c0cce5e).

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1-6 illustrate various examples related to SKU2VEC, according to aspects of the disclosure.

FIGS. 7-12 illustrate various examples related to embeddings, according to aspects of the disclosure.

FIGS. 13-22 illustrate various examples related to clustering, according to aspects of the disclosure.

FIGS. 23-35 illustrate various examples related to transference estimation, according to aspects of the disclosure.

FIGS. 36-39 illustrate various examples related to pricing, according to aspects of the disclosure.

FIGS. 40-41 illustrate example flow and code that can be used in estimate transference/exclusivity, according to aspects of the disclosure.

FIGS. 42-43 illustrate example flow and code that can be used in creating SKU embedding adjusting for assortment differences, according to aspects of the disclosure.

FIGS. 44-45 illustrate example flow and code that can be used in multi-banner embedding projection, according to aspects of the disclosure.

FIGS. 46-47 illustrate example flow and code that can be used to help find optimal embedding dimensions, according to aspects of the disclosure.

FIG. 48 illustrates an example computer 405, according to some aspects of the disclosure.

FIG. 49 illustrates an example of customer segmentation, according to aspects of the disclosure.

FIG. 50 illustrates an example of optimizing a range across brands, according to aspects of the disclosure.

FIG. 51 illustrate an example of finding and ranking the most important attributes that differentiate customers, according to aspects of the disclosure.

FIG. 52 illustrates an example of embedding of a collection using averaging, according to aspects of the disclosure.

FIG. 53 illustrates an example of how to use SKU embeddings for range decision, according to aspects of the disclosure.

DETAILED DESCRIPTION OF ASPECTS OF THE DISCLOSURE Word2Vec

Word2vec can be a technique for expressing words as vectors. With word2vec, words with similar meaning can be shown to have similar vectors. In addition, word2vec can capture elements of the meaning of the words so that word arithmetic can be used. For example, FIG. 1 illustrates several vectors for various words.

In FIG. 1, the top row indicates that if we take the vector for “king” and subtract from each number in it the number in the corresponding position in the vector for “queen,” then add the numbers in the “woman” vector and try to find the vector which has the closest numbers to the answer we just calculated, it turns out the vector we find corresponds to “queen.” Of course this can be the instinctive “human” answer for an English speaker.

Doc2Vec

Doc2Vec can be a technique used to create a numeric representation of a document, regardless of its length. But unlike words, documents do not come in logical structures such as words, so the another document-unique vector is used. Thus, in Doc2Vec, instead of using just words to predict the next word, document features are also used. Thus, when training the word vectors, a document vector D is trained as well, and in the end of training, it holds a numeric representation of the document. More information on Doc2Vec can be found in the following document, which is herein incorporated by reference in its entirety: Le, Quoc, and Tomas Mikolov. “Distributed representations of sentences and documents.” International conference on machine learning. 2014 (found at: https://medium.com/wisio/a-gentle-introduction-to-doc2vec-db3e8c0cce5e).

SKU2Vec

FIGS. 1-6 illustrate various examples related to SKU2VEC, according to aspects of the disclosure. In aspects of the disclosure, a neural network can be used with a computer system such that the neural network can be trained to answer one of two questions: 1) Is word A likely to be found in the same sentence as word B (‘bag of words model’)? And 2) Is word A likely to be found at a distance of 1, 2, . . . n words from word B, in the same sentence (‘skip-gram model’)? The sentences used for training may be a large sample of the language in question (e.g., the whole of Wikipedia in English).

Sku2vec can be analogous to word2vec, where “words” can be replaced by “SKUs” and “sentences” can be replaced by, for example, “SKUs”, such as SKUs bought in a same transaction (e.g., “baskets”),” in a same store, or by a customer, or any combination thereof. SKU, which stands for “Stock Keeping Unit” is an acronym that can be used in retail to mean a granular description of an item sold by a retailer. For example: “Hovis granary seeded brown thick-sliced bread, 400 g” and “Tropicana organic orange juice with juicy bits 750 ML” can be (hypothetical) descriptions of a grocery SKU. The “Tropicana organic orange juice with juicy bits 750 ML” example SKU can be transformed to an example vector of 200-dimension, e.g. [−0.18607, 0.161613, 0.304672, . . . 1.69594, −0.493748].

Online and/or physical stores can have thousands or millions of SKUs for the inventory in the online store and/or in the physical store.

SKU2Vec can help with retail analytics. In some aspects of the disclosure, the method can require minimal data and infrastructure. In some aspects, only data that are most commonly found in all retailers can be used, and the data can be run on a local computer. SKU2Vec can be applied in many retail cases, and can help solve client's challenges that could not be done with existing approaches.

With sku2vec, SKUs can be represented as vectors. The vectors can be a sequence of numbers (e.g., 100 numbers, or any other amount of numbers). In some aspects of the disclosure, SKU order may not be relevant in a shopping basket. Thus, for example, a “bag of words” model may be more applicable in this case than, for example, a “skip-gram” model. (See, e.g., the following references for background information: Mikolov, Tomas, et al. “Distributed representations of words and phrases and their compositionality.” Advances in neural information processing systems. 2013. Mikolov, Tomas, et al. “Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781 (2013).) However, in other aspects of the disclosure, SKU order may be relevant.

FIG. 1 illustrates how a semantic relationship can be captured by translating words to vectors. FIG. 2 illustrates how SKU2Vec can be inspired from natural language processing. FIG. 3 illustrates how SKU2Vec can utilize the vast amount of transaction data in retail to generate insight. For example, one can define an item based on what it is bought together with. One can require only customer basket purchase data. One can use an AI algorithm to convert each SKU to a series of numbers. Then one can correctly map out product relationships for all SKUS. FIG. 4 illustrates how SKUs that are bought in similar baskets may be close substitutes. FIGS. 5-6 illustrates several example cases.

In some aspects of the disclosure, doc2vec can also be used to with SKU2vec to group “words” (SKUs) together to represent collections of SKUS (everything customer bought, everything a store sold, or everything in basket, or any combination thereof).

Embeddings Examples

FIGS. 7-12 illustrate various examples related to embeddings, according to aspects of the disclosure. Embeddings may be helpful in some embodiments. Embeddings (e.g., vector representations of SKUs) can reduce something with a large number of dimensions (e.g., whether or not a SKU is one of many thousands of possibilities) to a smaller number (e.g., 100 numbers, where the similarity between items has real-world meaning).

FIG. 7 illustrates how SKU2Vec can be a modified version of Word2Vec, according to aspects of the disclosure. FIG. 8 illustrates how embedding can perform better at reducing bias caused by assortment differences across stores. FIG. 9 illustrates how banner assortment differences can be found by applying embedding (e.g., multi-banner supervised embedding). (A banner can be a different brand of a store. For example, Old Navy and Gap can be different banners owned by the same company. Multi-banner supervised embedding can be used to generate a single embedding that covers multiple banners.) FIG. 10 illustrates how SKU2Vec can product more robust results than Word2Vec in aspects of the disclosure. FIG. 11 illustrates how an example optimal embedding dimension can be estimated for a large supermarket chain, according to aspects of the disclosure. FIG. 12 illustrates examples of applying multilingual supervised embedding to help derive uniform product grouping across retail chains where non-overlapping SKUs occur frequently.

FIGS. 42-43 illustrate example flow and code that can be used in creating SKU embedding adjusting for assortment differences, according to aspects of the disclosure. FIG. 43 illustrates example code that can be used to set up the neural network. FIG. 42 illustrates example steps that can be taken to use the neural network to create SKU embeddings adjusting for assortment differences. Note that the steps can be done in other orders than those shown in the figures.

FIGS. 44-45 illustrate example flow and code that can be used in multi-banner embedding projection, according to aspects of the disclosure. FIG. 45 illustrates example code that can be used to set up the neural network. FIG. 44 illustrates example steps that can be taken to use the neural network for multi-banner embedding projection. Note that the steps can be done in other orders than those shown in the figures.

FIGS. 46-47 illustrate example flow and code that can be used to help find optimal embedding dimensions, according to aspects of the disclosure. FIG. 47 illustrates example code that can be used to set up the neural network. FIG. 46 illustrates example steps that can be taken to use the neural network to find optimal embedding dimensions. Note that the steps can be done in other orders than those shown in the figures.

In some aspects of the disclosure, collections of SKUs can be embedded. This can be done using averaging. FIG. 52 illustrates an example of embedding of a collection using averaging, according to aspects of the disclosure. Embedding collections of SKUs can be done using a DOC2Vec analysis, as shown in FIG. 43 (B3).

Clustering Examples

FIGS. 13-22 illustrate various examples related to clustering, according to aspects of the disclosure. FIG. 13 illustrates an example of how SKU2Vec can help improve product hierarchies and identify store clusters. FIG. 14 illustrates an example of how SKU2Vec can help identify wrongly categorized SKUs. FIG. 15 illustrates an example of how a list of potentially misclassified SKUs can be generated. FIG. 16 illustrates an example of how SKU2Vec can help re-organize categories according to how a consumer sees them. FIG. 17 illustrates an example of new categorisation that is aligned with consumer purchasing habits. FIG. 18 illustrates an example of how SKU2Vec can help optimize subcategories to better reflect customer need states. FIG. 19 illustrates an example of how current sub-categories may not always sufficiently capture consumer purchase preferences. FIG. 20 illustrates an example of how SKU2Vec can help identify store clusters. FIG. 21 illustrates an example of clear division between Western top income stores and non-Western top income stores. FIG. 22 illustrates an example of how algorithm insights may be combined with buyers' expertise to create a maximum impact. FIGS. 50-51 illustrate an example of finding and ranking the most important attributes that differentiate customers, according to aspects of the disclosure.

Example clustering algorithms that can be used include, but are not limited to: t-SNE (see, e.g., Maaten, Laurens van der, and Geoffrey Hinton, “Visualizing data using t-SNE,” Journal of machine learning research, 9 November (2008): 2579-2605, which is herein incorporated by reference in its entirety); and alternative methods (K-means, hierarchical clustering, etc.) described, for example, in the Aug. 20, 2020 Wikipedia article on cluster analysis (e.g., see https://en.wikipedia.org/wiki/Cluster analysis), which is herein incorporated by reference in its entirety.

Transference Examples

FIGS. 23-35 illustrate various examples related to transference estimation, according to aspects of the disclosure. FIG. 23 illustrates an example of how exclusivity can be approximated by looking at the uniqueness of the basket an item is bought in. FIG. 24 illustrates an example of how uniqueness of a basket can be derived using SKU embeddings. FIG. 25 illustrates an example formula of how to calculate exclusivity with SKU2Vec. FIG. 26 illustrates an example of how new exclusivity can derive similar answers compared to existing methods. FIGS. 27-28 illustrates an example of how a new approach can underestimate non-fresh SKUs due to consumer behaviour. FIG. 29 illustrates an example of an alternative method where transference can also be directly estimated if one can reliably infer out-of-stock SKUs. FIG. 30 is an example method to infer out-of-stock items. FIGS. 31-32 are consumer choice modelling examples. FIG. 33 is an example transference formula. FIGS. 34-35 are example model for delivering transference estimates and making sense of individual SKUs.

FIGS. 40-41 illustrate example flow and code that can be used in estimate transference/exclusivity, according to aspects of the disclosure. FIG. 41 illustrates example code that can be used to set up the neural network. FIG. 40 illustrates example steps that can be taken to use the neural network system to estimate transference and/or exclusivity. Note that the steps can be done in other orders than those shown in the figures.

Range Examples

Overall Range Design. The system can identify whether there are excessive and/or inadequate products in a range of products from the density of products in “embedding space.”. For example: Are there a lot of products with very similar embeddings? Are some of the products out on their own? Image vectors can be created so that they can be seen as 3D dots in space. We can take the vectors from lengths of hundreds to vector lengths of 2. Information can be thrown away or deleted. Clouds can be close to each other and separated from other clouds. There may be an area where we have lots of similar items. Sales can be spread between a lot of similar items. There can be some products in the 3D space that are attracting a lot of sales, so we can offer products similar to those. We can measure how close the embeddings are to measure sets of products. There can be very similar ones. There can be some that are very distinct. We can copy the popular products with a brand of their own. For example, if an Asian market sells 25 different types of Korean noodles that are very similar, customers are likely buying a random type of noodle. Thus, the store could cut down from 25 to 2 or 3, and customers may not care.

In some aspects of the disclosure, the system can be used to identify product gaps (e.g., a homebrand version of a product (where there's a complete gap in “embedding space”).

Allocation of Range to Stores. A store can have an embedding based on the products it sells, whether overall or in some categories. The system can take the average of the embeddings of the SKUs it sells. The system can also train the SKU embedding model to embed the store (e.g., Will this product be found in a basket with that product in this store?). The stores can then be clustered and these clusters can reveal customer preferences. This can provide insights that can help when designing ranges for stores. For example, it can help the store stock items that reflect local tastes which might be otherwise difficult to uncover from sales data. Stores in the same cluster can be given similar ranges.

FIG. 53 illustrates an examples of how to use SKU embeddings for range decision, according to aspects of the disclosure.

FIG. 54 illustrates an example on optimizing a range across brands, according to aspects of the disclosure.

Pricing Examples

FIGS. 36-39 illustrate various examples related to pricing, according to aspects of the disclosure. Elasticity measures can be useful in setting optimal non-promoted and/or promoted prices. These can be done at a SKU level (e.g., What happened when we promoted and/or repriced this before?). But if the price hasn't changed, or the SKU is new, or has never been price promoted (e.g., for fashion items which need to be marked down for clearance), it can be difficult to estimate. It can also be difficult to account for substitution (e.g., If we reduce the price of 2 pints of milk, what happens to sales of 1 pint of milk?). The system can help determine what happens if an entity prices to promote items. For example: how much has the store sold, how big was the reduction, and how much a store sell. In some cases, if a store has never discounted something before, and the store may need guidance regarding how much it will sell if it marks something down. For example, the store could see how black t shirts would sell, or sequin shirts. The system can produce an embedding of what the store has sold. The system can produce a math model that can predict the elasticity (e.g., change in units sold per change in price) of SKUs, from their embedding. It can be the changing quantity divided by the changing price. The system can calculate what sales the store has done before using the SKUs. The system can make the vector size 1 instead of 200 (for elasticity), in order to get an estimate for something that's been sold but never discounted, using what the store has previously sold and discounted. All that may be needed is to know its embedding.

FIGS. 36-37 illustrates an example of how SKU embedding can help estimate SKU's mark-down efficiency and how it can contain useful information to help estimate cannibalization. FIG. 38 illustrates an example of how true mark-down coefficients can be estimated using selected popular SKUs and SKU pairs. FIG. 39 illustrates an example of how SKU can pair with high cosine similarity more likely to have high cross-price sensitivity, but with large variance.

Embeddings can allow us to build elasticity and/or substitution models which may not be SKU-by-SKU but instead may be based on the elements of the embedding vector. For cross-elasticity (e.g., substitution), the system can use features of nearby vectors (e.g., How many are nearby? How far away is the nearest on each dimension of the embedding?). A substitution model can help the store determine what to stock and/or the elasticity related to pricing.

Embeddings can help the system cope with small amounts of data (e.g., the effect of price changes on other items can be used to infer the price effect on an item). Items which have never had price variation or markdown can have price response estimates.

Segmentation Examples

FIG. 49 illustrates an example of customer segmentation, according to aspects of the disclosure. Algorithms can be used with the output of the neural network to create customer segmentation.

For example, output of the neural network can be used with a segmentation algorithm to create customer segmentation. The SKU embeddings can be determined from the neural network. Then the SKUs can be clustered. The SKU vector can be, for example, 200 numbers. (Note that many other size vectors may be used.) Then, the average can be determined. For example, we can determine the distance from 200 numbers and find the ones the are closest to each other. If we want to cluster stores, we can look at the average embedding of all the SKUs that a particular store sold. And that gives us an embedding that can be the same form as the embedding for the SKU (e.g., 200 numbers), such as, for example, that shown in FIG. 21.

Once we have the clusters of SKUs and/or collections of SKUs, we can inspect them and determine segments. For example, a person can look at the clusters of SKUs or collections or SKUS and determine what information is important or interesting relating to the customer, the basket, or the store, or any combination thereof. For example, we can review information about all the items the customer bought. We can determine many metrics from the clusters of SKUs (e.g., take the average that took no account of quantity, take average of items bought in certain time period, take average of items bought in certain categories, ignore some products bought in different stores).

We can also determine many metrics from the collections of SKUs. Collections of SKUs can have embeddings as well as individual SKUs. We can do this by averaging the embeddings of the individual SKUs or by using the Doc2Vec analogy. If we use the Doc2Vec analogy, then both the SKU embeddings and at least one other embedding (e.g., store, customer, basket or any combination thereof) can be obtained. The average of all the embeddings can be used, or a Doc2Vec type analysis may be done. With respect to the average of the embeddings, this can be the same size (e.g., 200) as the SKU vector. With respect to the Doc2Vec, vectors for customers can be of a same or different size (e.g., 30) than the SKU vector (e.g., size 200).

Once we have the SKU clustering and/or the SKU collection information, we can color the dots in the clusters according to what we know about the SKUs. For example, the dots on FIG. 49 can each represent a customer. If we are clustering customers, the dot can represent a customer. If we are clustering items, the dot can represent an item. If we are clustering baskets, the dot can represent a basket. If we are clustering stores, the dot can represent a store. Dots that are close together can have similar embeddings.

The SKU collection information can use an algorithm (e.g., a principal component analysis) in addition to the algorithms used to cluster to find and rank the most important product attributes that differentiate the customers. Each SKU can be scored using these attributes. For example, FIG. 51 illustrates an example for wines from a liquor supermarket. This example can show that the additional algorithm found an abstract product attribute that is important to customers (in this example, the most important), which is the price of the product. In addition, a principal component analysis can find the most important dimensions of variability in the embeddings.

In some aspects of the disclosure, the representation of the vectors can comprise clustering of the vectors. In some aspects, the representation of the vectors can be expressed using principal components. In some aspects, the clustering can utilize embeddings related to a collection of SKUs. In some aspects, the representation of the vectors can comprise clustering of the vectors. In some aspects, the representation of the vectors can be expressed using principal components. In some aspects, the clustering can utilize embeddings related to a collection of SKUs.

Example Features

The techniques presented in this application may improve upon other options in the following ways.

It may provide a more robust and flexible approach to generate SKU embedding. Compared to other methods used to generate a SKU embedding (often based on matrix factorisation), SKU2VEC can digest much more data (e.g., it can include all basket transactions, compared to matrix factorisation which may only include a subset, or an aggregated version of it), with fewer assumptions (e.g., it may not don't use any existing product hierarchy). It may also have a more flexible structure. For example, the neural network in SKU2VEC can adjust the bias caused by different assortments across stores. This can be is achieved by feeding the neural network with extra information on which store the basket is purchased at, and the store's SKU assortment (see. e.g., FIG. 7).

It can derive price elasticity or markdown effectiveness on SKUs that could may not have previously been performed, due to lack of price changes. Other methods to estimate price elasticity/markdown effectiveness may require the SKU in scope to have experienced price changes (e.g., previously discounted). With SKU2VEC, the embeddings can be used directly to estimate the elasticity or markdown effectiveness on SKUs that have not previously experienced price changes. This may be done without the strong (and sometimes unrealistic) assumption that SKUs in the same human created product category show same/similar uplift when discounted. Instead SKU2VEC can use uses revealed preference from customers to estimate uplift (see, e.g., FIGS. 36-39).

It can derive transference estimates that could may not previously have been performed, using basket data without a customer identifier. Other methods of estimating transference can require a customer identifier, so to track the same customer making multiple shopping trips within a period, SKU2VEC introduces can use a technique that doesn't require a customer identifier, and only requires a basket identifier (see, e.g., FIGS. 23-24). This may help because for many retailors customer identifiers are may be hard to obtain and often missing.

It may increase accuracy in creating customer, store and SKU segmentations, by using more data and less assumptions. Other segmentation methods may rely on existing product attributes (e.g., product hierarchy, which may be a set of assumptions made by retailers about which products are similar), while SKU2VEC doesn't. SKU2VEC instead may provide measures of similarity which can be based on actual customer behaviour, without the leap of faith needed to create the product hierarchy.

It may increases the accuracy of range decisions by using data that was unavailable before. SKU2VEC can introduce a new approach in range optimisation by using the SKU embeddings, which may not have not been used in prior range decision methods. This approach may involve calculating the vector distance between embeddings as a proxy for SKU similarity, which then can be used to map out the entire product landscape. This similarity measure can be is derived purely from actual customer behaviour.

Methods and systems for displaying information related to items stocked in a store can be provided. The methods and systems can comprise: accepting, using a computer system, stock keeping unit (SKU) information for items in the store, and basket information indicating which SKUs were sold together in a same basket; defining dimension information and structure information for a neural network; training the neural network using the basket information indicating which SKUs were sold together in the same basket to calculate weight values and bias values for the neural network; generating a vector for each SKU using the neural network; and displaying a representation of the vectors that illustrates information related to the items stocked in the store. The neural network may not need customer identification information and/or product information other than the SKU.

Methods and systems for displaying information related to pricing information for items in a store can be provided. The system can be used with the example computer system described below. The system can use modules to perform the various steps described in aspects of the disclosure. The methods and systems can comprise: accepting, using a computer system, stock keeping units (SKUs) for the items in the store; defining dimension information and structure information for a neural network; training the neural network to create weight values and bias values for the neural network; generating a vector for each SKU using the neural network; and displaying a representation of the vectors that illustrates information related to pricing for the items in the store. The pricing can comprise regular pricing and/or promotional pricing. The neural network may not need customer identification information. Basket information can indicate which items were sold together in a basket. The neural network may use historical pricing information.

Methods and systems for displaying information related to items purchased in a pre-designated store can be provided. The methods and systems can comprise: accepting, using a computer system, stock keeping unit (SKU) information for items in the store, and basket information indicating which SKUs were sold together in the same basket, and in which pre-designated store the basket was sold; defining dimension information and structure information for a neural network; training the neural network using the basket information indicating which SKUs were sold together in the same basket to calculate weight values and bias values for the neural network; generating a vector for each SKU using the neural network; generating a vector for each pre-designated store using the vectors for each SKU or directly from the neural network; and displaying a representation of the vectors that illustrates information related to the items purchased in the pre-designated store. The methods and systems can also determine which items are from the same store. Exclusive negative SKUs selected only from the SKUs available in the store where the basket was purchased can also be determined. Each SKU can comprise words and/or numbers. Embeddings utilizing the exclusive negative SKUs to transfer the SKUs into vectors can be created using the neural network. The exclusive negative SKUs can be SKUs not found with another SKU together in a basket. Groups of items that were and were not sold together in baskets can be determined. Embeddings can be used with the neural network. The embeddings can comprise information about the items and information about where the items were purchased. The neural network may not need customer identification information. The systems and methods can determine which store sells the SKUs in the same basket using the basket information and the store identifier information. The methods and systems can utilize encoding with the neural network to recommend which assortment of items should be sold in which store. The neural network can be used for transference estimation. The neural network can be used to determine optimal embedding dimensions.

Example Computer System

The subject matter described herein can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structural means disclosed in this specification and structural equivalents thereof, or in combinations of them. The subject matter described herein can be implemented as one or more computer program products, such as one or more computer programs tangibly embodied in an information carrier (e.g., in a machine readable storage device), or embodied in a propagated signal, for execution by, or to control the operation of, data processing apparatus (e.g., a programmable processor, a computer, or multiple computers). A computer program (also known as a program, software, software application, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file. A program can be stored in a portion of a file that holds other programs or data, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flows described in this specification, including the method steps of the subject matter described herein, can be performed by one or more programmable processors (e.g., processor 410 in FIG. 48) executing one or more computer programs to perform functions of the subject matter described herein by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus of the subject matter described herein can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

FIG. 48 illustrates an example computer 405, according to some embodiments of the present disclosure. Computer 405 can include a processor 410 suitable for the execution of a computer program, and can include, by way of example, both general and special purpose microprocessors, and any one or more processor of any kind of digital computer. A processor can receive instructions and data from a memory 430 (e.g., a read only memory or a random access memory or both). Processor 410 can execute instructions and the memory 430 can store instructions and data. A computer can include, or be operatively coupled to receive data from or transfer data to, or both, a storage medium 440 for storing data (e.g., magnetic, magneto optical disks, or optical disks). Information carriers suitable for embodying computer program instructions and data can include all forms of nonvolatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, flash memory device, or magnetic disks. The processor 410 and the memory 430 can be supplemented by, or incorporated in, special purpose logic circuitry. The computer 405 can also include an input/output 420, a display 450, and a communications interface 460.

The computer may be used with a computing device that may include but is not limited to personal computers, servers, smart phones, media players, electronic tablets, mobile devices, email devices, etc.

Any processor(s) 410 used may use any known processor technology, including but not limited to graphics processors and multi-core processors. Suitable processors for the execution of a program of instructions may include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors or cores, of any kind of computer. Generally, a processor may receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer may include a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer may also include, or be operatively coupled to communicate with, one or more non-transitory computer-readable storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data may include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in, ASICs (application-specific integrated circuits).

The input 420 may be any known input device technology, including but not limited to a keyboard (including a virtual keyboard), mouse, track ball, and touch-sensitive pad or display. To provide for interaction with a user, the features and functional operations described in the disclosed embodiments may be implemented on a computer having a display 450 such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device such as a mouse or a trackball by which the user can provide input to the computer. Display 450 may be any known display technology, including but not limited to display devices using Liquid Crystal Display (LCD) or Light Emitting Diode (LED) technology.

Communications interface 460 may be configured to enable the computing device to communicate with other computing or network device across a network, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. For example, the communications interface may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like.

The memory 430 may be any computer-readable medium that participates in providing computer program instructions and data to processor(s) for execution, including without limitation, non-volatile storage media (e.g., optical disks, magnetic disks, flash drives, etc.), or volatile storage media (e.g., SDRAM, ROM, etc.). Memory 430 may include various non-transitory computer-readable instructions for implementing an operating system (e.g., Mac OS®, Windows®, Linux), network communication, and Application(s) and program modules. The operating system may be multi-user, multiprocessing, multitasking, multithreading, real-time, and the like. The operating system may perform basic tasks, including but not limited to: recognizing input from input devices; sending output to the display; keeping track of files and directories on the memory; controlling peripheral devices (e.g., disk drives, printers, etc.) which can be controlled directly or through an I/O controller; and managing traffic on a bus. The bus may be any known internal or external bus technology, including but not limited to ISA, EISA, PCI, PCI Express, NuBus, USB, Serial ATA or FireWire.

Network communications instructions may be used with or by the computer 405 and may establish and maintain network connections (e.g., software applications for implementing communication protocols, such as TCP/IP, HTTP, Ethernet, telephony, etc.).

Application(s) and program modules may be used with or by the computer 405 and may include software application(s) and different functional program modules which are executed by processor(s) 41 to implement the processes described herein and/or other processes. The program modules may include but not limited to software programs, objects, components, data structures that are configured to perform particular tasks or implement particular data types. The processes described herein may also be implemented in the operating system.

Communication between various network and computing devices may be used with or by the computer 405 and may be facilitated by one or more application programming interfaces (APIs). APIs of the system may be proprietary and/or may be examples available to those of ordinary skill in the art such as Amazon® Web Services (AWS) APIs or the like. The API may be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on a call convention defined in an API specification document. A parameter may be a constant, a key, a data structure, an object, an object class, a variable, a data type, a pointer, an array, a list, or another call.

The described features and functional operations described in the disclosed embodiments may be implemented in a computer system that includes a back-end component, such as a data server, or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a user computer having a graphical user interface or an Internet browser, or any combination thereof. The components of the system may be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include, e.g., a telephone network, a LAN, a WAN, and the computers and networks forming the Internet.

The computer system may include user computing devices and application servers. A user or client computing device and server may generally be remote from each other and may typically interact through a network. The relationship of client computing devices and server may arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

The neural network described herein may use machine learning and/or artificial intelligence to improve the field of retail analytics by, for example, finding patterns and relationships that would otherwise be impossible to determine using a very large data set. In addition, the machine learning can also continuously use new data to modify and/or update retail analytic information. The machine learning and/or artificial intelligence may include the study of computer algorithms that improve automatically through experience. Machine learning/artificial intelligence algorithms can build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. More information on machine learning/artificial intelligence can be found in the Aug. 18, 2020 Wikipedia article https://en.wikipedia.org/wiki/Machine_learning, which is herein incorporated by reference in its entirety.

CONCLUSION

It is to be understood that the disclosed subject matter is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. The disclosed subject matter is capable of other embodiments and of being practiced and carried out in various ways. Accordingly, other implementations are within the scope of the following claims. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting. As such, those skilled in the art will appreciate that the conception, upon which this disclosure is based, may readily be utilized as a basis for the designing of other structures, methods, and systems for carrying out the several purposes of the disclosed subject matter. It is important, therefore, that the claims be regarded as including such equivalent constructions insofar as they do not depart from the spirit and scope of the disclosed subject matter.

Although the disclosed subject matter has been described and illustrated in the foregoing exemplary embodiments, it is understood that the present disclosure has been made only by way of example, and that numerous changes in the details of implementation of the disclosed subject matter may be made without departing from the spirit and scope of the disclosed subject matter.

In addition, it should be understood that any figures which highlight the functionality and advantages are presented for example purposes only. The disclosed methodology and system are each sufficiently flexible and configurable such that they may be utilized in ways other than that shown. For example, other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. In addition, the steps or components may be used in a different order or configuration than that shown in the drawings.

Although the term “at least one” may often be used in the specification, claims and drawings, the terms “a”, “an”, “the”, “said”, etc. also signify “at least one” or “the at least one” in the specification, claims and drawings.

Finally, it is the applicant's intent that only claims that include the express language “means for” or “step for” be interpreted under 35 U.S.C. 112(f). Claims that do not expressly include the phrase “means for” or “step for” are not to be interpreted under 35 U.S.C. 112(f). 

1. A computer-implemented system for displaying information related to items from a pre-designated store, comprising: a processor in communication with a network, the processor configured for: accepting stock keeping unit information for items in the pre-designated store, and combination information indicating which stock keeping units were sold together, and store information indicating which pre-designated store sold the combination, wherein each stock keeping unit comprises words and/or numbers; defining dimension information and structure information for a neural network; training the neural network using the combination information indicating which stock keeping units were sold together in the same combination, but not requiring person identification, to calculate weight values and bias values for the neural network; generating a vector for each stock keeping unit using the neural network; generating a vector for each pre-designated store using the vectors for each stock keeping unit or directly from the neural network; and displaying a representation of the vectors that illustrates information related to the items from the pre-designated store; wherein the stock keeping unit embeddings are determined from the neural network, and wherein the stock keeping units are clustered using the embeddings.
 2. The system of claim 1, wherein the processor is configured for: determining exclusive negative stock keeping units selected only from the stock keeping units available in the pre-designated store where the combination; creating embeddings utilizing the exclusive negative stock keeping units to transfer the stock keeping units into vectors, wherein the exclusive negative stock keeping units are stock keeping units not found with another stock keeping unit together in a combination; and utilizing the embeddings with the neural network, the embeddings comprising information about the items and information about stores where the items were sold; wherein the stock keeping unit embeddings are determined from the neural network, and wherein the stock keeping units are clustered using the embeddings\.
 3. The system of claim 1, wherein the processor is configured for: determining which items are from the same store.
 4. The system of claim 1, wherein the processor is configured for: determining, using the computer-implemented system, groups of items that were and were not sold together in combinations.
 5. The system of claim 1, wherein the processor is configured for: determining, using the computer-implemented system, which store sells the stock keeping units in the same combination using the combination information and the store identifier information.
 6. The system of claim 1, wherein the processor is configured for: utilizing encoding with the neural network to recommend which assortment of items should be sold in which store.
 7. The system of claim 1, wherein the processor is configured for: using the neural network for transference estimation.
 8. The system of claim 1, wherein the processor is configured for: using the neural network to determine optimal embedding dimensions.
 9. A computer-implemented system for displaying information related to items stocked in a store, comprising: a processor configured for: accepting, at a computer in communication with a network, stock keeping unit information for items in the store, and combination information indicating which stock keeping units were sold together in a same combination, wherein each stock keeping unit comprises words and/or numbers; defining, at the computer, dimension information and structure information for a neural network; training, at the computer, the neural network using the combination information indicating which stock keeping units were sold together in the same combination to calculate weight values and bias values for the neural network; generating, at the computer, a vector for each stock keeping unit using the neural network; and displaying, at the computer, a representation of the vectors that illustrates information related to the items stocked in the store, wherein the neural network does not need person identification information and/or product information other than the stock keeping unit; wherein the stock keeping unit embeddings are determined from the neural network, and wherein the stock keeping units are clustered using the embeddings.
 10. A computer implemented system for displaying information related to pricing information for items in a store, comprising: a processor in communication with a network, the processor configured for: accepting stock keeping units for the items in the store; defining dimension information and structure information for a neural network; training the neural network to create weight values and bias values for the neural network; generating a vector for each stock keeping unit using the neural network; and displaying a representation of the vectors that illustrates information related to pricing for the items in the store; wherein the neural network uses combination information indicating which items were sold together in a combination, but wherein the neural network does not need person identification information.
 11. The system of claim 10, wherein the pricing comprises regular pricing.
 12. The system of claim 10, wherein the pricing comprises promotional pricing.
 13. The system of claim 10, wherein the neural network uses historical pricing information. 